Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity)
Language: English
Published by Princeton University Press, 2007
- Softcover
- Used

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Book is considered to be in acceptable condition. The actual cover image may not match the stock photo. Book may have one or more of the following defects: noticeable wear on the cover dust jacket or spine; curved, dog eared or creased page s ; writing or highlighting inside or on the edges; sticker s or other adhesive on cover; CD DVD may not be included; and book may be a former library copy.
Seller Inventory # SEWV.0691127026.A
- Title
- Complex Adaptive Systems: An Introduction to Computational Models of Social Life (Princeton Studies in Complexity)
- Author
- Miller, John H.; Page, Scott
- Publisher
- Princeton University Press
- Publication year
- 2007
- Condition
- acceptable
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0691127026
- ISBN 13
- 9780691127026
- Series
- Book 2 of 16: Princeton Studies in Complexity
This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents.
John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended.
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